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Andreas talks at SemperVirens: Why traditional workforce analytics don't scale

HR Transformation
Blogpost
Watch TechWolf's Andreas and Synopsys's Jill discuss skills intelligence breakthroughs at SemperVirens. Learn how AI discovered 70% skills overlap leadership never knew existed.
Contents
July 29, 2025
3 min read

At the SemperVirens Summit 2025, a fireside discussion titled "Evolving Workforce Transformation: Mastering Skills Inventory (with AI-driven strategies)" brought together industry leaders to tackle one of the most pressing challenges facing modern enterprises. Moderated by Raquel Scott, Senior Associate at Semper Virens, the panel featured Andreas De Neve, Co-Founder and CEO of TechWolf; Jill Larsen, Chief People Officer at Synopsys; Parker Mitchell, CEO of Valence; and Jordana Kammerund, CHO of Corning.

While the discussion covered AI coaching and leadership development, the most compelling insights emerged from the Andreas-Jill dialogue on skills visibility at scale. Their conversation revealed why traditional methods fail and what actually works when you're managing skills for tens of thousands of employees.

Key takeaways

The scale problem is real

"Maintaining a skills database by survey doesn't work at this scale," Andreas explained. With tens of thousands of workers each having dozens of evolving skills, manual tracking becomes impossible.

Inference beats input


TechWolf's breakthrough: analyzing existing work data (JIRA tickets, project contributions) to automatically infer skills rather than relying on self-assessments. "We look at data that's very close to the work that's actually happening."

Hidden talent discovery

At Synopsys, TechWolf's analysis of 4,000 employees revealed 70-80% skills overlap between sales engineers and product engineers—insight no leader suspected, unlocking massive internal mobility opportunities.

Skills as infrastructure

Jill's strategic approach: treating skills data as foundational infrastructure rather than another HR application. "We're going to build the skills platform through TechWolf...very similar as a data lake."

Game-changing Results

From the TechWolf analysis, we actually learned that about 70 to 80% of the jobs are similar and the skills are similar between our AE population [sales engineers] and our product engineering population—which none of our leaders knew and we didn't know. So what that means is we can actually have a lot more velocity and agility between those two populations. The cost of productivity of moving people around is a lot faster than the cost of productivity of hiring from the outside in a very rare skill set environment.
Jill Larsen, Chief People Officer, Synopsys

Get in touch

Ready to explore skills intelligence for your organization? Connect with TechWolf's team to discuss your specific challenges and opportunities.

Andreas talks at SemperVirens: Why traditional workforce analytics don't scale

July 29, 2025
3 min read
Contents

At the SemperVirens Summit 2025, a fireside discussion titled "Evolving Workforce Transformation: Mastering Skills Inventory (with AI-driven strategies)" brought together industry leaders to tackle one of the most pressing challenges facing modern enterprises. Moderated by Raquel Scott, Senior Associate at Semper Virens, the panel featured Andreas De Neve, Co-Founder and CEO of TechWolf; Jill Larsen, Chief People Officer at Synopsys; Parker Mitchell, CEO of Valence; and Jordana Kammerund, CHO of Corning.

While the discussion covered AI coaching and leadership development, the most compelling insights emerged from the Andreas-Jill dialogue on skills visibility at scale. Their conversation revealed why traditional methods fail and what actually works when you're managing skills for tens of thousands of employees.

Key takeaways

The scale problem is real

"Maintaining a skills database by survey doesn't work at this scale," Andreas explained. With tens of thousands of workers each having dozens of evolving skills, manual tracking becomes impossible.

Inference beats input


TechWolf's breakthrough: analyzing existing work data (JIRA tickets, project contributions) to automatically infer skills rather than relying on self-assessments. "We look at data that's very close to the work that's actually happening."

Hidden talent discovery

At Synopsys, TechWolf's analysis of 4,000 employees revealed 70-80% skills overlap between sales engineers and product engineers—insight no leader suspected, unlocking massive internal mobility opportunities.

Skills as infrastructure

Jill's strategic approach: treating skills data as foundational infrastructure rather than another HR application. "We're going to build the skills platform through TechWolf...very similar as a data lake."

Game-changing Results

From the TechWolf analysis, we actually learned that about 70 to 80% of the jobs are similar and the skills are similar between our AE population [sales engineers] and our product engineering population—which none of our leaders knew and we didn't know. So what that means is we can actually have a lot more velocity and agility between those two populations. The cost of productivity of moving people around is a lot faster than the cost of productivity of hiring from the outside in a very rare skill set environment.
Jill Larsen, Chief People Officer, Synopsys

Get in touch

Ready to explore skills intelligence for your organization? Connect with TechWolf's team to discuss your specific challenges and opportunities.

Recording SemperVirens Summit 2025

Watch Andreas ( CEO TechWolf) & Jill Larsen ( Chief People Officer at Synopsys) reveal how AI uncovered millions in hidden workforce potential, including the moment Jill exposes a 70% skills overlap across 4,000 employees that leaders never saw.

Watch the full SemperVirens discussion
Watch the full SemperVirens discussion

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Using AI while interviewing at Techwolf

At TechWolf, we see generative AI as part of the modern toolkit — and we expect candidates to treat it that way too. We love it when people use AI to take their thinking to the next level, rather than to replace it.You are welcome to use tools like ChatGPT, Claude, or others during our interview process, especially in take-home assignments or technical exercises. We encourage you to bring your full toolkit — and that includes AI — as long as it reflects your own thinking, decisions and creativity.We don’t see AI as replacing your skills. Instead, we’re interested in how you use it: to brainstorm ideas, speed up iteration, validate your thinking, or unlock new ways of approaching a challenge. Great candidates show judgment in when to rely on AI, how to adapt its output, and where to go beyond it.

What we’re looking for:

Our interviews are designed to understand how you think, solve problems, and express ideas. Using AI in a way that amplifies those things — not masks them — is encouraged.

What to avoid:

We ask that you don’t submit AI-generated work without review, or present answers that you can’t fully explain. We’re not testing the model — we’re getting to know you, your skills, and your potential. If there are cases where we don’t want you to use AI for something, we’ll tell you ahead of the interview being booked.In short: use AI as you would on the job — as a smart assistant, not a stand-in.

Example: Programming with AI

In a coding challenge, you’re welcome to use generative AI to support your workflow — just like you might in a real development environment. For instance, you might use AI to quickly generate boilerplate code, look up syntax, or get a first-pass solution that you then adapt and debug collaboratively. What we’re interested in is your ability to reason through trade-offs, communicate clearly, think about complexity and iterate effectively — not whether you memorized the syntax perfectly. If using AI helps you stay in flow and focus on higher-level problem-solving, we consider that a strength. There could be some challenges where we won’t allow you to use AI - in that case we’ll tell you in advance, and will tell you why.

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